March 2026 arXiv papers — page 46

Chenglong Song, Mazharul Islam, Lin Wang, Bing Chen

Conditional density estimation (CDE) is a fundamental task in machine learning that aims to model the full conditional law $\mathbb{P}(\mathbf{y} \mid \mathbf{x})$, beyond mere point prediction (e.g., mean, mode). A core challenge is free-form density estimation, capturing distributions that exhibit multimodality, asymmetry, or topological complexity without

Ruichao Yang, Wei Gao, Xiaobin Zhu, Jing Ma

Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We present Probabilistic Concept Graph Reasoning (PCGR), an interpretable and evolvable framework that reframes multimodal misinformation detection (MMD) as structured and concept-based re

Shaojin Bai, Yuting Su, Weizhi Nie

Cross-site generalizability in medical AI is fundamentally compromised by selection bias, a structural mechanism where patient demographics (e.g., age, severity) non-randomly dictate hospital assignment. Conventional Domain Generalization (DG) paradigms, which predominantly target image-level distribution shifts, fail to address the resulting spurious correl

Sagnik Basu, Subhrajit Mitra, Aman Juneja, Somnath Banerjee

Recent research points toward LLMs being manipulated through adversarial and seemingly benign inputs, resulting in harmful, biased, or policy-violating outputs. In this paper, we study an underexplored issue concerning harmful and toxic mathematical word problems. We show that math questions, particularly those framed as natural language narratives, can serv

Jizhong Zhang, Fazle Hussain, Jie Yao

Spanwise wall oscillation (SWO) of turbulent boundary layers (TBLs) is investigated via direct numerical simulations over an extended actuation region with oscillation periods up to T_{sc}^+=600, scaled by the uncontrolled friction velocity u_{\tau 0} at the onset of SWO (i.e. Re_\theta=344). For low periods (T_{sc}^+<200), drag reduction (DR) decreases with

Peng Wen, Yuting Wang, Qiurui Wang

Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football. TacSIm imitates the acitons of all 11 player

Umair Siddique

As AI assistants become integrated into safety engineering workflows for Physical AI systems, a critical question emerges: does AI assistance improve safety analysis quality, or introduce systematic blind spots that surface only through post-deployment incidents? This paper develops a formal framework for AI assistance in safety analysis. We first establish

Andong Tan, Shuyu Dai, Jinglu Wang, Fengtao Zhou

Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations. To address this gap, we introduce CPGBench, an autom

Takumi Kato, Masato Kikuchi, Tadachika Ozono

Effective instruction in tutoring requires promptly providing instructional materials that match the needs of each student (e.g., in response to questions). In this study, we introduce an agent that automatically delivers supplementary materials on demand during one-on-one tutoring sessions. Our agent uses a multimodal large language model to analyze spoken

Marvin Seyfarth, Sarah Kaye Müller, Arman Ghanaat, Isabelle Ayx

Latent diffusion models (LDMs) have recently achieved strong performance in 3D medical image synthesis. However, modalities like cine cardiac MRI (CMR), representing a temporally synchronized 3D volume across the cardiac cycle, add an additional dimension that most generative approaches do not model directly. Instead, they factorize space and time or enforce

Ya-Bing Zuo, Ming-Ge Li, Shi-Yu Liang, Wan-Ting Liu

In the present work, the form factors of $B_{(s)}$ to light P-wave axial vector mesons are calculated via the light cone sum rules (LCSR) in the framework of heavy quark effective field theory (HQEFT). Firstly, the expressions of form factors in terms of the light cone distribution amplitudes (DAs) of axial vector mesons are derived via the LCSR at the leadi

Bingfang Li, Songhao Yang, Qinglan Wang, Xu Zhang

Deploying synchronous condensers (SynCons) near grid-following renewable energy sources (GFLRs) is an effective and increasingly adopted strategy for grid support. However, the potential transient instability risks in such configurations remain an open research question. This study investigates the mechanism of dominant synchronization instability source tra

Simon Brezovnik, Janez Žerovnik

In this paper, we study the $[k]$-Roman domination number of cylindrical graphs $C_m \Box P_n$. Our analysis begins with a general lower bound based on local neighborhood constraints, showing that $\gamma_{[k]R}(C_m\Box P_n) > (k+1)\left\lceil\frac{mn}{5}\right\rceil.$ By exploiting the connection between $[k]$-Roman domination and efficient domination, we c

Yeongju Bak

Public blockchains impose an inherent tension between regulatory compliance and user privacy. Existing on-chain identity solutions require centralized KYC attestors, specialized hardware, or Decentralized Identifier (DID) frameworks needing entirely new credential infrastructure. Meanwhile, over four billion active X.509 certificates constitute a globally de

Jaione Bengoetxea, Itziar Gonzalez-Dios, Rodrigo Agerri

Recent research on dialectal NLP has identified data scarcity as a primary limitation. To address this limitation, this paper presents a catalog of contemporary Basque dialectal data and resources, offering a systematic and comprehensive compilation of the dialectal data currently available in Basque. Two types of data sources have been distinguished: online

Jiahao Wang, Hualian Sheng, Sijia Cai, Yuxiao Yang

Identity-preserving video generation offers powerful tools for creative expression, allowing users to customize videos featuring their beloved characters. However, prevailing methods are typically designed and optimized for a single identity reference. This underlying assumption restricts creative flexibility by inadequately accommodating diverse real-world

Yifan Luo, Kangping Xu, Yanzhen Lu, Yang Yuan

Persona-driven large language models (LLMs) require consistent behavioral tendencies across interactions to simulate human-like personality traits, such as persistence or reliability. However, current LLMs often lack stable internal representations that anchor their responses over extended dialogues. This work explores whether LLMs can maintain "implicit con

Adam Jakobsen, Sushant Gautam, Hugo Lewi Hammer, Susanne Olofsdotter

AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To address this issue, we present a zero-shot, knowledge-guided framework for psychiatric tabular data in which large language models (LLMs) are steered via Retrieval-Augmented Genera

Yunjeong Lee, Jongho Park, Do-Young Byun, Minchul Kam

The East Asia VLBI Network (EAVN) has recently enabled dual-polarization observations at $22$ and $43\,\mathrm{GHz}$. We present the first systematic verification of its polarimetric performance using EAVN observations of M87, 3C 279, 3C 273, and OJ 287, calibrated with the GPCAL pipeline and evaluated against near-contemporaneous VLBA images at comparable f

Ying Li, Xinglin Lyu, Junhui Li, Jinlong Yang

Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextual signals are unevenly beneficial across sentences. Existing training objectives do not explicitly model this variability, limiting a model's ability to adaptively exploit context. In this paper, we propose Cross

Théo Dumont, Théo Lacombe, François-Xavier Vialard

We study the estimation of optimal transport (OT) maps between an arbitrary source probability measure and a log-concave target probability measure. Our contributions are twofold. First, we propose a new evolution equation in the set of transport maps. It can be seen as the gradient flow of a lift of some user-chosen divergence (e.g., the KL divergence, or r

Marvin Seyfarth, Salman Ul Hassan Dar, Yannik Frisch, Philipp Wild

Diffusion models have become a leading approach for high-fidelity medical image synthesis. However, most existing methods for 3D medical image generation rely on convolutional U-Net backbones within latent diffusion frameworks. While effective, these architectures impose strong locality biases and limited receptive fields, which may constrain scalability, gl

Petr Lazar, Michal Otyepka

Single-atom catalysts (SACs), composed of isolated metal atoms dispersed on solid supports, represent the ultimate expression of atomic efficiency in catalysis. Their remarkable activity and selectivity arise from local coordination environments and adjustable oxidation states, yet precise determination of these features remains an enduring challenge. Among

Wanjiang Weng, Xiaofeng Tan, Xiangbo Shu, Guo-Sen Xie

Text-to-motion generation holds significant potential for cross-linguistic applications, yet it is hindered by the lack of bilingual datasets and the poor cross-lingual semantic understanding of existing language models. To address these gaps, we introduce BiHumanML3D, the first bilingual text-to-motion benchmark, constructed via LLM-assisted annotation and

Luciana Angiuli, Simone Ferrari

We investigate the hypercontractivity property of generalized Mehler semigroups on the $L^p$-scale with respect to invariant measures. This property is first obtained in the purely theoretical setting of skew operators and, subsequently, deduced for generalized Mehler semigroups arising from linear stochastic differential equations perturbed by L\'evy noise.

Hieu Xuan Le, Benjamin Goh, Quy Anh Tang

Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production, guardrails must mitigate these attacks under strict low-latency constraints, resulting in a deployment gap in which lightweight classifiers and rule-based systems struggle to generalize under distribution shift, whil

Md Mushfiqur Azam, John Quarles, Kevin Desai

Egocentric 3D human pose estimation remains challenging due to severe perspective distortion, limited body visibility, and complex camera motion inherent in first-person viewpoints. Existing methods typically rely on single-frame analysis or limited temporal fusion, which fails to effectively leverage the rich motion context available in egocentric videos. W

Daniel Duverney, Iekata Shiokawa

Let $t\geq2$ and $k\geq1$ be integers. Let $H_{k}(z)$ with $\left\vert z\right\vert <1$ be the limit of a certain subsequence of the Stern polynomials introduced by Dilcher and Eriksen. We use Mahler's method to prove the algebraic independence of the values at nonzero algebraic points of the functions $H_{k}(z)$ and $H_{k}(z^{t^{k}})$.

Rong-Fang Liu, Wan-Lu Song, Wan-Li Yang, Hua Guan

Exploiting quantum effects for energy storage, quantum batteries (QBs) offer compelling advantages over conventional ones in terms of superior energy density, ultrafast charging, and high conversion efficiency. However, their realization is hampered by decoherence, which causes incomplete charging, rapid self-discharging, and reduced extractable work. Here,

Quentin Rible, Stéphane Seuret

This paper investigates the traces of functions belonging to the inhomogeneous Besov spaces B $\xi$ p,q , where $\xi$ is a product of capacities defined as powers of Gibbs measures. We first establish that the traces of functions in B $\xi$ p,q along affine hyperplanes belong to another inhomogeneous Besov space. Furthermore, we derive an upper bound for the

Richard P. Behiel

In 1973, Bardeen, Cater, and Hawking published "The Four Laws of Black Hole Mechanics", establishing the mathematical framework that would later be understood as the thermodynamics of black holes. Central to the paper is equation (33), which writes the variation of the total energy-momentum integral in terms of physically meaningful quantities: angular momen

Shiji Zhao, Shukun Xiong, Maoxun Yuan, Yao Huang

In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both common corruptions and adversarial examples, leading to potential security risks. To improve the robustness of infrared object detection, current methods mostly adopt a data-driven id

Marina Sánchez-Torrón, Daria Akselrod, Jason Rauchwerk

LLM performance is highly sensitive to prompt design, yet whether automatic prompt optimization can replace expert prompt engineering in linguistic tasks remains unexplored. We present the first systematic comparison of hand-crafted zero-shot expert prompts, base DSPy signatures, and GEPA-optimized DSPy signatures across translation, terminology insertion, a

Songhao Yang, Bingfang Li, Zhiguo Hao, Yiwen Hu

In traditional views, the build-up of accelerating energy during faults can cause the well-known first-swing angle instability in synchronous generators (SGs). Interestingly, this letter presents a new insight that the accumulation of decelerating energy due to the low voltage ride-through (LVRT) and recovery control of grid-following inverter-based resource

Meshv Patel, Bikash Baro, Sayan Bayan, Mohendra Roy

Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occlusion, computational costs, and physical constraints. This work presents a comparison of machine learning (ML) and deep learning models for a custom triboelectric nanogenerator (TENG)-based sensor glove. Utili

Imen Tounsi, Fadi Karkafi, Mohammed El Badaoui, François Guillet

Mechanical vibration monitoring often requires high sampling rates and generates large data volumes, posing challenges for storage, transmission, and power efficiency. Compressive Sensing (CS) offers a promising approach to overcome these constraints by exploiting signal sparsity to enable sub-Nyquist acquisition and efficient reconstruction. This study pres

Mutong Liu, Yang Liu, Jiming Liu

Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the intervention strategies for controlling infectious disease spread and responding to outbreaks in recent year

Bin Yang, Mohamed Abdelsamad, Miao Zhang, Alexandru Paul Condurache

Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Existing approaches emphasize semantic awareness by enforcing feature consistency across augmented views or by masked scene modeling. However, the resulting representations transfer poorly to instance localization,

Haozhen Wang, Haoyue Liu, Jionghao Zhu, Zhichao Wang

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as outdated knowledge and the tendency to generate hallucinations. To address these limitations, Retrieval-Augmented Generation (RAG) systems have been introduced, enhancing LLMs with

Kumar Ashutosh, Chi Hsuan Wu, Kristen Grauman

Current large-scale video datasets focus on general human activity, but lack depth of coverage on fine-grained activities needed to address physical skill learning. We introduce SportSkills, the first large-scale sports dataset geared towards physical skill learning with in-the-wild video. SportSkills has more than 360k instructional videos containing more t

Marie Braasch, Anna Kartashova, Elena Goi, Thomas Pertsch

Diffractive neural networks (DNNs) are an emerging approach for the realization of photonic artificial intelligence, especially due to their suitability for machine-vision applications and high-dimensional photonic information processing at lower power consumption. However, incorporating optical nonlinear activation functions to make DNNs a feasible alternat

Shumpei Nishida, Kunihisa Okano

This paper proposes a distributed event-triggered control method that not only guarantees consensus of multi-agent systems but also satisfies a given LQ performance constraint. Taking the standard distributed control scheme with all-time communication as a baseline, we consider the problem of designing an event-triggered communication rule such that the resu

Masayo Fujimura, Matti Vuorinen

We study the well-known Ptolemy-Alhazen problem on reflection of light at the surface of a spherical mirror in the case when the source of light is very far from the mirror.

SuYeon Kim, Wongyu Lee, MyeongAh Cho

3D anomaly detection targets the detection and localization of defects in 3D point clouds trained solely on normal data. While a unified model improves scalability by learning across multiple categories, it often suffers from Inter-Category Entanglement (ICE)-where latent features from different categories overlap, causing the model to adopt incorrect semant

Sergey Smirnov

Minimal Kitaev chains provide a unique platform to engineer Majorana states in quantum dots interacting via normal tunneling and crossed Andreev reflection specified by their amplitudes $|\eta_{n,a}|$. Here we analyze fluctuations of electric currents in a double quantum dot Kitaev chain using the differential effective charge $q$, that is the ratio of the d

Chengyu Fang, Heng Guo, Zheng Jiang, Chunming He

Multimodal large language models are promising for clinical visual question answering tasks, but scaling to 3D imaging is hindered by high computational costs. Prior methods often rely on 2D slices or fixed-length token compression, disrupting volumetric continuity and obscuring subtle findings. We present Photon, a framework that represents 3D medical volum

Jielei Ni, Yao Zhang, Qianyi Wei, Zhangyu Zhou

Spatiotemporal optical vortices (STOVs) carry transverse orbital angular momentum and offer new degrees of freedom for light-matter interactions. Yet conventional focusing of STOVs introduces spatiotemporal astigmatism: the beam diffracts while the pulse duration stays constant, causing the vortex to deform away from focus. Here we overcome this limitation b

H. Moradpour, S. Jalalzadeh, R. Jalalzadeh, A. H. Ziaie

Recently, a fractional version of the Schwarzschild-Tangherlini black hole with a fractal horizon has been introduced. Motivated by the key role of the Schwarzschild solution in gravitational and astrophysical studies, some consequences of this fractional-fractal generalization of the Schwarzschild black hole have been investigated. In this line, the corresp

E. A. Dzhenzher, S. V. Dzhenzher, V. Zh. Sakbaev

In this paper the random channels and their compositions in the space of quantum states are studied. For compositions of i.i.d. random unitary channels, the limit behaviour of probability distributions is described. The sufficient condition for convergence in probability is obtained. The generalized convergence in distribution w.r.t. weak operator topology i

Jeremy H. M. Wong, Nancy F. Chen

In speech evaluation, an Automatic Speech Recognition (ASR) model often computes time boundaries and phoneme posteriors for input features. However, limited data for ASR training hinders expansion of speech evaluation to low-resource languages. Open-source weakly-supervised models are capable of ASR over many languages, but they are frame-asynchronous and no

Haihua Liang, Jianfeng Huang

This paper studies the number of limit cycles, known as the Smale-Pugh problem, for the generalized Abel equation \begin{align*} \frac{dx}{d\theta}=A(\theta)x^p+B(\theta)x^q, \end{align*} where $A$ and $B$ are are piecewise trigonometrical polynomials of degree $ m $ with two zones $0\leq\theta<\theta_1$ and $\theta_1\leq\theta\leq2\pi$. By means of the firs

Jiong Li, Ji Jiang, Qing-Hu Chen

The superconducting diode effect (SDE), characterized by nonreciprocal critical currents, has attracted growing attention due to its potential applications in quantum technologies and energy-efficient devices. In this work, we explore the microscopic mechanism of the SDE by simulating asymmetric multilayer heterostructures within time-dependent Ginzburg-Land

Vehid Geruslu, Zulfiyya Aliyeva, Eray Tüzün

Context: The rapid adoption of AI-assisted code generation tools, such as large language models (LLMs), is transforming software development practices. While these tools promise significant productivity gains, concerns regarding the quality, reliability, and security of AI-generated code are increasingly reported in both academia and industry. --Objective: T

Ansel Blume, Burak Uzkent, Shalini Chaudhuri, Garin Kessler

Direct preference optimization (DPO) is an effective technique to train language models to generate preferred over dispreferred responses. However, this binary "winner-takes-all" approach is suboptimal for vision-language models whose response quality is highly dependent on visual content. In particular, a response may still be faithful to the visual inputs

Jiseung Hong, Benjamin G. Ascoli, Jinho D. Choi

Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codebases through either agentic exploration or full-context generation. Existing benchmarks capture a broad range of coding capabilities, such as resolving GitHub issues, but none of them directly isolate and measure how effectively LLMs leverage reposito

Palanivel Raman, Ramaswamy Radha, Pankaj Kumar Mishra, Paulsamy Muruganandam

We present an analytical and numerical study of the dynamics and stability of exciton-polariton condensates described by the open-dissipative Gross-Pitaevskii equation, incorporating both binary and short-range three-body interactions. Using an asymptotic description, we identify the parameter regime and derive equations for the instability amplitude, provid

Roberto Memeo, Davide Piras, Roberto Osellame, Andrea Crespi

Femtosecond-laser direct waveguide writing is progressively emerging as an alternative to conventional techniques to develop complex photonic devices, for applications ranging from classical and quantum information processing, to sensing and metrology. Laser written waveguides typically offer low modal birefringence, thus preserving coherence of polarization

Christian Voigt

These notes are an introduction to the theory of quantum symmetries of finite and infinite sets, graphs, and locally compact spaces.

Luanrong Chen, Renzhi Chen, Xinyu Li, Shanshan Li

Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and struggle to adapt to evolving design requirements, potentially causing structural drift and costly full regeneration. We propose IncreRTL, a LLM-driven framework for incremental RTL generation under requirement evo

Sahibzada Adil Shahzad, Ammarah Hashmi, Junichi Yamagishi, Yusuke Yasuda

Multimodal deepfakes can exhibit subtle visual artifacts and cross-modal inconsistencies, which remain challenging to detect, especially when detectors are trained primarily on curated synthetic forgeries. Such synthetic dependence can introduce dataset and generator bias, limiting scalability and robustness to unseen manipulations. We propose SAVe, a self-s

Dengdi Chen, Yan Zheng

We demonstrate that Lagrangian flow for the 2D Boussinesq equations under degenerate noise exhibit chaotic behavior characterized by the strict positivity of the top Lyapunov exponent, where the degenerate noise acts only on a few Fourier modes of the temperature equation. To achieve this, we overcome difficulties arising from the degeneracy of noise and its

Haruki Kawase, Taiga Sugawara, A. Daniel Carnerero

Accurate forecasting of future solar irradiance is essential for the effective control of solar thermal power plants. Although various kriging-based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in

Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu, Marco Fanizza

In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While extensively studied clas

Fan Bu, Yiqun Chen, Tuomas Hytönen, Dachun Yang

We develop a comprehensive theory for a general class of multi-parameter function spaces of Besov-Triebel-Lizorkin type, with a matrix weight. We prove the equivalence of different quasi-norms, the identification of function and sequence spaces via the $\varphi$-transform, the boundedness of almost diagonal operators and multi-parameter singular integrals un

San-Dong Guo, Yang Liu

The hidden altermagnetism has been theoretically proposed and then experimentally confirmed in metal $\mathrm{Cs_{1-\delta}V_2Te_2O}$, which exhibits two nearly degenerate ground-state magnetic configurations (C-type and G-type) corresponding respectively to apparent and hidden altermagnetism. Here, we propose that in-plane uniaxial strain can be utilized to

Taegyoon Yoon, Yegyu Han, Seojin Ji, Jaewoo Park

Smart glass is emerging as an useful device since it provides plenty of insights under hands-busy, eyes-on-task situations. To understand the context of the wearer, 6D object pose estimation in egocentric view is becoming essential. However, existing 6D object pose estimation benchmarks fail to capture the challenges of real-world egocentric applications, wh

Ngo Tan Phuc

In this paper, we study the Graded Invariant Basis Number (grIBN) property for Leavitt path algebras of finite graphs. Using the talented monoid as our main tool, we establish a complete matrix-theoretic characterization of when a Leavitt path algebra of a finite graph fails to have gr-IBN. Consequently, we identify several classes of graphs whose Leavitt pa

Tianjun Pan, Xuan Lin, Wenyan Yang, Qianyu He

Rubric-based evaluation has become a prevailing paradigm for evaluating instruction following in large language models (LLMs). Despite its widespread use, the reliability of these rubric-level evaluations remains unclear, calling for meta-evaluation. However, prior meta-evaluation efforts largely focus on the response level, failing to assess the fine-graine

Yinjian Wang, Wei Li, Yuanyuan Gui, James E. Fowler

Robust principal component analysis (RPCA) seeks a low-rank component and a sparse component from their summation. Yet, in many applications of interest, the sparse foreground actually replaces, or occludes, elements from the low-rank background. To address this mismatch, a new framework is proposed in which the sparse component is identified indirectly thro

Yaowen Chang, Zhen Cao, Xu Zheng, Xiaoxin Mi

Panoramic semantic segmentation is pivotal for comprehensive 360{\deg} scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation. While Unsupervi

Jiawang Zhang, Fengxiang Zhao, Kun Xu

This study proposes a novel adaptive finite volume-particle method (AFVPM) for accurate and efficient free surface flow simulations. The proposed AFVPM synergistically combines the Eulerian finite volume method (FVM) on unstructured meshes with the Lagrangian smoothed particle hydrodynamics (SPH) approach. Specifically, the mesh-based FVM is employed in the

Chinonso Onah, Obinna Uzoh, Obinna Abah

We present a measurement-based quantum thermal machine that extracts work from the back-action of generalized quantum measurements whose working medium is a coupled two-level quantum system. Specifically, we derive universal optimization criteria for a three-stroke measurement-based engine cycle with coupled two-level system of Ising-like interaction as a wo

F. R. Ferraro, E. Vesperini, B. Lanzoni, D. Romano

The discovery of the complex stellar populations hosted in two massive stellar systems in the Galactic bulge, namely Terzan5 and Liller 1, posed intriguing questions about their origin. Despite their globular cluster appearance, they host sub-populations with significantly different ages (several Gyrs) and metallicities (about 1 dex) tracing a chemical abund

Zhuorui Wang, Jun Li

We investigate a hybrid photon blockade (HPB) scheme in a driven two-qubit cavity QED system arising from the combination of eigenenergy-level anharmonicity (ELA) and quantum destructive interference (QDI). By tuning the detuning of a single qubit and pumping field, we identify precise parametric regimes that fully integrate the advantages of high brightness

Kulkarni Namratha, S. Pushpavanam

Non-uniform product (color) distribution in colorimetric paper-based sensors affects the accuracy and reliability of measurements. The underlying mechanisms responsible for this are still unclear. The coffee ring effect explains the ring-formation at the periphery. However, ring-like patterns can also be found at intermediate radial positions in these sensor

M. Zhezhu, A. Vasil'ev, M. Yaprintsev, A. Musayelyan

Phase change materials based on Ge-Sb-Te alloys are widely explored for their potential in both memory devices and thermoelectric applications. In this study, films of Ge2Sb2Te5 (GST) doped with varying concentrations of Pb were prepared and systematically investigated to trace the effect of Pb doping on crystallization-induced phase transformations and ther

Guojie Li, Wuyue Yang, Liu Hong

Physics-informed neural networks (PINNs) have been proven as a promising way for solving various partial differential equations, especially high-dimensional ones and those with irregular boundaries. However, their capabilities in real applications are highly restricted by their poor generalization performance. Inspired by the rigorous mathematical statements

Jin Chen, Yifeng Lin, Chao Zeng, Si Wu

The standardization of vibrotactile data by IEEE P1918.1 workgroup has greatly advanced its applications in virtual reality, human-computer interaction and embodied artificial intelligence. Despite these efforts, the semantic interpretation and understanding of vibrotactile signals remain an unresolved challenge. In this paper, we make the first attempt to a

Junkai Jiang, Yitao Xu, Ruochen Li, Shaobing Xu

The Collaborative Task Sequencing and Multi-Agent Path Finding (CTS-MAPF) problem requires agents to accomplish sequences of tasks while avoiding collisions, posing significant challenges due to its combinatorial complexity. This work introduces CTS-PLL, a hierarchical framework that extends the configuration-based CTS-MAPF planning paradigm with two key enh

Ryo Misawa, Shunsuke Kitou, Jian-Ping Sun, Yingpeng Yu

Competing charge and spin orders are central to uncovering the nature of unconventional superconductivity. Here we utilize synchrotron X-ray diffraction on a high-quality single crystal to reveal the charge order of La$_3$Ni$_2$O$_7$ at ambient pressure, which competes with the high-temperature superconducting phase under pressure. Enabled by the high synchr

Jiawei Lin, Wanrong Zhu, Vlad I Morariu, Christopher Tensmeyer

Document generation has gained growing attention in the field of AI-driven content creation. In this work, we push its boundaries by introducing AnyDoc, a framework capable of handling multiple generation tasks across a wide spectrum of document categories, all represented in a unified HTML/CSS format. To overcome the limited coverage and scale of existing h

Alice Rizzardo, Julie Symons, Michel Van den Bergh

We present a general procedure for constructing triangulated categories, linear over a field, with distinct enhancements. Some of our examples can be equipped with a (non-degenerate) t-structure, thereby showing that the existence of a t-structure does not imply uniqueness of enhancements, whether in the strong or weak sense (depending on the example).

Zhuo Cheng, Changfeng Gui, Yeyao Hu, Qinfeng Li

We study the first nontrivial Steklov eigenvalue of perimeter-normalized regular \(N\)-gons and show that it is strictly increasing in \(N\). The proof mainly relies on an analytic framework that establishes a refined asymptotic expansion in three steps: first, identifying the Steklov eigenvalue as the maximal eigenvalue of a Toeplitz-type operator; second,

Kazuhiro Sato

Controllability scores provide principled information on where intervention should be applied in large-scale network systems when explicit control design is difficult. Two representative controllability scores are the volumetric controllability score (VCS) and the average energy controllability score (AECS). While both are important, the standard AECS treats

Guojie Li, Liu Hong

Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their performance and physical consistency strongly depend on how governing laws are incorporated. In this work, we present a systematic comparison of different thermodynamic structure-informed neural networks by inc

Ayman El Zein, Maidoun Mortada

For a non-decreasing sequence $S=(s_1,s_2,\dots,s_k)$, an $S$-packing coloring of a graph $G$ is a vertex coloring using the colors $s_1,s_2,\dots,s_k$ such that any two vertices assigned the same color $s_i$ are at distance greater than $s_i$. A subcubic graph is said to be $k$-saturated, for $0\le k\le3$, if every vertex of degree 3 is adjacent to at most

Lomash Relia, Jai G Singla, Amitabh, Nitant Dube

This study presents a vision system for planetary rovers, combining real-time perception with offline terrain reconstruction. The real-time module integrates CLAHE enhanced stereo imagery, YOLOv11n based object detection, and a neural network to estimate object distances. The offline module uses the Depth Anything V2 metric monocular depth estimation model t

Debangshu Banerjee, Changming Xu, Eugene Ie, Ming Zhang

Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric models, including LLMs, which are then tuned per task to improve performance. However, existing self-evolving agent frameworks provide no formal

Yuto Matsuo, Yoshihiro Fukuhara, Yuki M. Asano, Rintaro Yanagi

Data augmentation is a key technique for improving the robustness of image classification models. However, many recent approaches rely on diffusion-based synthesis or complex feature mixing strategies, which introduce substantial computational overhead or require external datasets. In this work, we explore a different direction: procedural augmentation based

Chenglong Wang, Yifu Huo, Yang Gan, Qiaozhi He

Recent advances in multimodal reward modeling have been largely driven by a paradigm shift from discriminative to generative approaches. Building on this progress, recent studies have further employed reinforcement learning from verifiable rewards (RLVR) to enhance multimodal reward models (MRMs). Despite their success, RLVR-based training typically relies o

Yuqiao Zeng, Xu Wang, Tengfei Liang, Yiqing Hao

Multimodal learning integrates complementary information from different modalities such as image, text, and audio to improve model performance, but its success relies on large-scale labeled data, which is costly to obtain. Active learning (AL) mitigates this challenge by selectively annotating informative samples. In multimodal settings, many approaches impl

Yoshiki Hiruta

Subcritical transition to turbulence, in which the laminar state is linearly stable yet finite-amplitude perturbations develop into turbulence, is ubiquitous but lacks a simple analytical framework. We demonstrate such a framework using a shell model of turbulence, in which external forcing breaks the phase symmetry of the governing equations. This symmetry

Suraj Racha, Prashant Harish Joshi, Utkarsh Maurya, Nitin Yadav

Large Language Models (LLMs) have shown remarkable capabilities for complex tasks, yet adaptation in medical domain, specifically mental health, poses specific challenges. Mental health is a rising concern globally with LLMs having large potential to help address the same. We highlight three primary challenges for LLMs in mental health - lack of high quality

Junyue Wang, Zhicheng Yao, Yan Pi, Xiaolong Li

Functional verification remains a critical bottleneck in modern IC development cycles, accounting for approximately 70% of total development time in many projects. However, traditional methods, including constrained-random and formal verification, struggle to keep pace with the growing complexity of modern semiconductor designs. While recent advances in Larg

Maxence Corman, William E. East, Jocelyn S. Read

While there are a number of proposed formation channels for subsolar mass compact objects, including black holes formed primordially, or neutron stars that form in collapsar disks, there have yet to be any conclusive observations of such objects. Motivated by the possibility that, if such objects exist, gravitational waves from binary mergers may reveal them

Xuanru Zhou, Yiwen Shao, Wei-Cheng Tseng, Dong Yu

Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally bottlenecked by its reliance on weak, noisy, and scale-limited labels. Drawing lessons from vision's foundational pre-training blueprint, we argue that the audio field must first establish its own large-scale,

Anbang Ruan

Existing multi-agent frameworks allow each agent to simultaneously plan, execute, and evaluate its own actions -- a structural deficiency we term the "Logic Monopoly." Empirical evidence quantifies the resulting "Reliability Gap": 84.30% average attack success rates across ten deployment scenarios, 31.4% emergent deceptive behavior without explicit reward si

Konstantinos Tsouvalas

Let $k$ be a nonarchimedean local field. For any $n\geq 3$, we construct the first examples of robust quasi-isometric embeddings of non-elementary free groups into $\mathsf{GL}_n(k)$ which are not limits of Anosov representations. If $\bf{K}=\mathbb{R},\mathbb{C}$, we exhibit examples of non-locally rigid, robust quasi-isometric embeddings of virtually free

Cristian Lupascu, Alexandru Lupascu

Large Language Model based agents increasingly operate in high stakes, multi turn settings where factual grounding is critical, yet their memory systems typically rely on flat key value stores or plain vector retrieval with no mechanism to track the provenance or trustworthiness of stored knowledge. We present ElephantBroker, an open source cognitive runtime

Junyuan Liu, Shuangjie Peng, Fulin Zhong

We prove that for any bounded convex domain $\Omega \subset \mathbb{R}^n$, the function \begin{equation*} \psi_\Omega(\xi) = \int_{\mathbb{R}^n\setminus\Omega} \frac{\mathrm{d}x}{|x-\xi|^{2n}}, \quad \xi\in\Omega, \end{equation*} has exactly one critical point. This confirms an conjecture proposed by Clapp, Pistoia and Salda\~na in [J. Math. Pures Appl. 205